In a field of pattern recognition, the following two methods are known as a method of performing pattern recognition on an input signal in which a separation point of a recognition unit is not clear. The first method is a method of dividing the input signal into a plurality of elements to be coupled to each other in accordance with a predetermined standard, and individually recognizing each element (hereinafter, this method is referred to as an “analytic method”). The second method is a method of performing recognition and division at the same time while considering every possibility of a division point of the input signal using a stochastic model such as a hidden Markov model (HMM) (hereinafter, this method is referred to as an “wholistic method”).
However, in the analytic method, temporarily divided elements are coupled to each other using a heuristic method, so that accuracy in recognition is not sufficiently secured in some cases. On the other hand, in the wholistic method, processing is performed while considering every possibility of the division point, so that a calculation amount is large, and a high-spec hardware resource is required. As described herein, the analytic method and the wholistic method each have a disadvantage, so that there is a demand for a novel technique in which such disadvantages are solved.